
"""simple docstring"""


import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				int:







       """simple docstring"""
       UpperCamelCase       			=		torch.load(UpperCAmelCase_		,   map_location="cpu"					)
       if "model" in sd.keys():
              UpperCamelCase       			=		torch.load(UpperCAmelCase_		,   map_location="cpu"					)['''model''']

       # pop unnecessary weights
       UpperCamelCase       			=		[
           '''decoder.version''',
           '''decoder.output_projection.weight''',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(UpperCAmelCase_					)

       UpperCamelCase       			=		{
           '''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
           '''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
           '''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
           '''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     UpperCamelCase       			=		sd.pop(UpperCAmelCase_					)

       UpperCamelCase       			=		list(sd.keys()					)
       for key in keys:
              if ".qkv_proj." in key:
                     UpperCamelCase       			=		sd[key]
                     # We split QKV in separate Q,K,V

                     UpperCamelCase       			=		key.replace(".qkv_proj."		,   ".q_proj."					)
                     UpperCamelCase       			=		key.replace(".qkv_proj."		,   ".k_proj."					)
                     UpperCamelCase       			=		key.replace(".qkv_proj."		,   ".v_proj."					)

                     UpperCamelCase       			=		value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     UpperCamelCase       			=		torch.split(UpperCAmelCase_		,   depth // 3		,   dim=0					)

                     UpperCamelCase       			=		q
                     UpperCamelCase       			=		k
                     UpperCamelCase       			=		v
                     del sd[key]

       return sd



@torch.no_grad()
def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_=None					)->				Optional[int]:







       """simple docstring"""
       UpperCamelCase       			=		load_checkpoint(UpperCAmelCase_					)

       if config is not None:
              UpperCamelCase       			=		OPTConfig.from_pretrained(UpperCAmelCase_					)
       else:
              UpperCamelCase       			=		OPTConfig()

       UpperCamelCase       			=		OPTModel(UpperCAmelCase_					).half().eval()
       model.load_state_dict(UpperCAmelCase_					)

       # Check results
       Path(UpperCAmelCase_					).mkdir(exist_ok=UpperCAmelCase_					)
       model.save_pretrained(UpperCAmelCase_					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--fairseq_path""",
          type=str,
          help=(
              """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
              """ https://huggingface.co/models?other=opt_metasq"""
          ),
      )
      parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
      parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
      SCREAMING_SNAKE_CASE			  =  parser.parse_args()
      convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)










'''simple docstring'''



from queue import PriorityQueue
from typing import Any

import numpy as np
def        UpperCAmelCase (    A	:					dict       ,		A	:					str       ,		A	:					set       ,		A	:					set       ,		A	:					dict       ,		A	:					dict       ,		A	:					PriorityQueue       ,		A	:					dict       ,		A	:					float | int       ,		):
							for nxt, d in graph[v]:
														if nxt in visited_forward:
																					continue
														SCREAMING_SNAKE_CASE       :   str         =      cst_fwd.get(A       ,		np.inf					)
														SCREAMING_SNAKE_CASE       :   Optional[int]         =      cst_fwd[v] + d
														if new_cost_f < old_cost_f:
																					queue.put((new_cost_f, nxt)					)
																					SCREAMING_SNAKE_CASE       :   Tuple         =      new_cost_f
																					SCREAMING_SNAKE_CASE       :   List[str]         =      v
														if nxt in visited_backward:
																					if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																												SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      cst_fwd[v] + d + cst_bwd[nxt]
							return shortest_distance
def        UpperCAmelCase (    A	:					str       ,		A	:					str       ,		A	:					dict       ,		A	:					dict					):
							SCREAMING_SNAKE_CASE       :   Dict         =      -1

							SCREAMING_SNAKE_CASE       :   Optional[int]         =      set()
							SCREAMING_SNAKE_CASE       :   Optional[Any]         =      set()
							SCREAMING_SNAKE_CASE       :   Any         =      {source: 0}
							SCREAMING_SNAKE_CASE       :   Tuple         =      {destination: 0}
							SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      {source: None}
							SCREAMING_SNAKE_CASE       :   Optional[int]         =      {destination: None}
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()
							SCREAMING_SNAKE_CASE       :   PriorityQueue[Any]         =      PriorityQueue()

							SCREAMING_SNAKE_CASE       :   List[str]         =      np.inf

							queue_forward.put((0, source)					)
							queue_backward.put((0, destination)					)

							if source == destination:
														return 0

							while not queue_forward.empty() and not queue_backward.empty():
														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   str         =      queue_forward.get()
														visited_forward.add(A					)

														SCREAMING_SNAKE_CASE       ,SCREAMING_SNAKE_CASE       :   int         =      queue_backward.get()
														visited_backward.add(A					)

														SCREAMING_SNAKE_CASE       :   Tuple         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														SCREAMING_SNAKE_CASE       :   Optional[int]         =      pass_and_relaxation(
														    A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		A       ,		)

														if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
																					break

							if shortest_distance != np.inf:
														SCREAMING_SNAKE_CASE       :   int         =      shortest_distance
							return shortest_path_distance


lowerCAmelCase_  :							int             =    {
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
lowerCAmelCase_  :							str             =    {
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
							import doctest

							doctest.testmod()




